Biodiv Sci ›› 2023, Vol. 31 ›› Issue (11): 23272.  DOI: 10.17520/biods.2023272

• Technology and Methodology • Previous Articles     Next Articles

Research progress of birdsong recognition algorithms based on machine learning

Xiaohu Shen1,2,*(), Xiangyu Zhu1, Hongfei Shi2, Chuanzhi Wang3   

  1. 1 Department of Forensic Science and Technology, Jiangsu Police Institute, Nanjing 210031
    2 National Forestry and Grassland Administration, Key Laboratory of State Forest and Grassland Administration on Wildlife Evidence Technology, Nanjing 210023
    3 iFLYTEK CO. LTD., Hefei 230088
  • Received:2023-07-31 Accepted:2023-10-12 Online:2023-11-20 Published:2023-12-08
  • Contact: * E-mail:


Background & Aim: Birds, located at the upstream of the ecological food chain, serve as crucial reference indicators for environmental quality and pollution. However, monitoring the status and trends of bird diversity in ecosystems poses a significant challenge. Establishing an all-weather bird diversity detection in system requires an extensively applicable machine learning-based birdsong recognition algorithm. To facilitate a precise comprehension of the research status pertaining to machine learning-based birdsong recognition algorithms and their developmental trends, we introduce the fundamental concepts of birdsong recognition and provides an overview of machine learning-based bird sound recognition algorithms from the perspective of model structure design.

Summary: Given the interdisciplinary nature of machine learning-based birdsong recognition technology, the algorithms can be classified into the following categories based on research directions: probabilistic model, template matching, time series analysis, transfer learning, data fusion, ensemble learning, metric learning-based, and unsupervised clustering birdsong recognition algorithms. We review the technical context of these categories in the context of performing birdsong recognition tasks. Furthermore, we present an analysis of the characteristics and limitations of these algorithms, along with a comparison of their birdsong recognition effectiveness in birdsong recognition. It also discusses commonly used standardized birdsong open-source datasets for birdsong and evaluation metrics applied. Finally, we outline the challenges confronted by existing methods and identifies potential future research directions in this field.

Perspectives: We endeavor to furnish scholars and developers involved in birdsong recognition research with a comprehensive reference framework, enabling them to better comprehend the existing technologies and potential developmental trends. Currently, there is a need to enhance the accuracy and robustness of machine learning-based birdsong recognition methods, especially for large-scale data samples. Additionally, the promotion and application of these methods still encounter several challenges that require resolution. The future investigations should focus on the following aspects: (1) optimization and improvement models; (2) integrating of multimodal data; (3) application of transfer learning; (4) expansion of application scenarios; and (5) establishing and standardization of databases.

Key words: birdsong recognition, machine learning, deep learning, bird diversity, birdsong datasets, evaluation metrics